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upgrade paths for AI-augmentation Design, implement, and test AI models with integrated protocols, or frameworks Evaluate performance, interoperability, and security implications of replacing classical EDA
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learning, dynamical systems, sequence generative models and world modelling. Emphasis will be on methods that design and implement new architectures for latent dynamic modelling to enable identification
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/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and eigenmode analysis for photonic crystals; terahertz/microwave far-field spectra measurements or near
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uncertainty in the ocean environment affect ambient noise and acoustic propagation, translating that understanding into new models, methods and software tools to support applications in maritime sensing
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foundation models, knowledge graph/ontology, federated learning or collaborative agents, AI security, etc.; (c) have experience in research proposal development; (d) have strong publication records in
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questions, advancing deep learning models, or other topics discussed with the PI. We use publicly available and simulated genomic data. Core job duties include: (1) Building computational pipelines
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over time. Develop projects aligned with your expertise, including computational phenotyping using natural language processing and large language models; germline genetic analyses, including GWAS, HLA
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a full Stage 2 application comprising a competitive PhD proposal with a detailed budget and to find relevant supervisors to adhere to the required supervision model described above. Up to five
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intelligence including model design, implementation, evaluation, system development Develop innovative models for video intelligence (e.g., multimodal understanding, embodied AI) Lead manuscript writing
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. This postdoctoral fellowship in the Arlotta lab involves computational analysis of large, multimodal single-cell datasets, as part of a collaborative project which also aims to generate AI and machine learning models